Deep HST Imaging in 47 Tucanae: A Global Dynamical Model
Bibliographic record
Abstract
Abstract Multi-epoch observations with the Advanced Camera Survey and WFC3 on the Hubble Space Telescope provide a unique and comprehensive probe of stellar dynamics within 47 Tucanae. We confront analytic models of the globular cluster with the observed stellar proper motions that probe along the main sequence from just above 0.8–0.1 M ⊙ as well as white dwarfs younger than 1 Gyr. One field lies just beyond the half-light radius where dynamical models (e.g., lowered Maxwellian distributions) make robust predictions for the stellar proper motions. The observed proper motions in this outer field show evidence for anisotropy in the velocity distribution as well as skewness; the latter is evidence of rotation. The measured velocity dispersions and surface brightness distributions agree in detail with a rotating anisotropic model of the stellar distribution function with mild dependence of the proper-motion dispersion on mass. However, the best-fitting models underpredict the rotation and skewness of the stellar velocities. In the second field, centered on the core of the cluster, the mass segregation in proper motion is much stronger. Nevertheless the model developed in the outer field can be extended inward by taking this mass segregation into account in a heuristic fashion. The proper motions of the main-sequence stars yield a mass estimate of the cluster of at a distance of 4.7 kpc. By comparing the proper motions of a sample of giant and subgiant stars with the observed radial velocities we estimate the distance to the cluster kinematically to be 4.29 ± 0.47 kpc.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".